News

When Adam Wierman joined Caltech’s faculty in 2007, he set out to find a new challenge. “I wanted to do something about a problem of fundamental importance,” he says. “Climate is the problem.” To help clean up computing, he decided to design new algorithms for the management of data centers, communication networks, and our power grid. He hoped to find ways to improve the energy efficiency of I.T. infrastructure. But these efforts lead to Jevons paradox—a variation of “If you build it, they will come.” Economist William Jevons wrote in 1865, “It is wholly a confusion of ideas to suppose that the economical use of fuel is equivalent to a diminished consumption.” In other words, as people like Wierman make computing and the grid more efficient, we use more, out-spending the savings. [Breakthrough story]

Graduate student Grant Van Horn and postdoctoral scholars Oisin Mac Aodha, working with Professor Pietro Perona, started the iNaturalist Challenge last year, to see how much they could push machine-learning technology. The competition is now in its second year and the dataset contains over 8,000 species, with a combined training and validation set of 450,000 images that have been collected and verified by multiple users from iNaturalist. This year's competition promise to be much more challenging because there are more species and less examples for the computer to learn from. The top submissions will be invited to give talks at CVPR, which is the premier annual computer vision event. [Enter the competition]

Four graduate students from the Computing and Mathematical Sciences (CMS) Department and one from the Electrical Engineering (EE) Department have been selected as 2017 Amazon Fellows. This fellows program is the result of a partnership between Caltech and Amazon AWS around Machine Learning and Artificial Intelligence. The EE fellow is Srikanth Tenneti who is exploring the potential of deep learning for Direction of Arrival applications, and extending Ramanujan Sums based techniques for multi-dimensional periodicity extraction. CMS graduate student Navid Azizan Ruhi is researching faster optimization algorithms for machine learning. He is looking forward to visiting Amazon AI as a fellow and exchanging ideas with their researchers. Computer science graduate student Hoang Le is developing methods for efficient and intelligent sequential decision making in realistic systems. Florian Schaefer, whose focus is applied and computational mathematics, is researching the interface of statistical estimation and the design of fast algorithms. Control and dynamical systems graduate student Ellen Feldman, working with Professor Joel Burdick, has used part of the funding to present her research at the Society for Neuroscience annual meeting and looking forward to other future opportunities to share her research.

Aadith Moorthy, a senior majoring in materials science and computer science, has been named to the inaugural class of Knight-Hennessy Scholars, a graduate-level scholarship program founded by Stanford University. The program aims to develop a community of future global leaders to address complex challenges through collaboration and innovation. Aadith will receive a scholarship providing full tuition, room and board, and a living stipend while he pursues a PhD in materials science. [Caltech story]

Caltech and Disney Research have entered into a joint research agreement to pioneer robotic control systems and further explore artificial intelligence technologies. Pietro Perona will work with Disney roboticist Martin Buehler to create navigation and perception software that could allow robotic characters to safely move through dense crowds and interact with people. Aaron Ames will work with Disney Research's Lanny Smoot to further explore robot autonomy and machine learning by creating objects that can self-navigate and perform stunts. Yisong Yue has been working with engineers from Disney Research on the use of machine learning to analyze the behavior of soccer players and to measure audience engagement. [Caltech story]

As she steps down as CEO of the Anita Borg Institute, Telle Whitney (PhD ’85) reflects on her career in tech—and the path ahead for the next generation of women. From Caltech to researcher to entrepreneur to advocate for women in technology, this Caltech alumna’s career has thrived on risk-taking and transition—and she’s inspired and assisted hundreds of thousands of women along the way. [Techer profile]

Animashree (Anima) Anandkumar, Bren Professor of Computing and Mathematical Sciences, and colleagues have won a Best Poster Award at the Neural Information Processing Systems (NIPS) MLtrain workshop. The submission was called “Tensor Regression Networks with TensorLy and MXNet” and the work showed that tensor contractions and regression layers are an effective replacement for fully connected layers in deep learning architectures. The MLtrain workshop focuses on making research more accessible through ipython notebooks and the submissions are judged based on the technical clarity and ease of understanding of the poster and the code. [View the poster]

Caltech senior Ching-Yun “Chloe” Hsu has won a 2018 Outstanding Undergraduate Researcher Award from the Computing Research Association (CRA). Her research interests are in theoretical computer science and the intersection of computer science and mathematics. She learned about the “3SUM Conjecture” when she took a course in computational complexity theory in her freshman year and her subsequent research on that problem has led to important new results that were published in the 42ndInternational Symposium on Mathematical Foundations of Computer Science in 2017. In addition, results of her research on the Discrete Fourier Transform were published in the Symposium on Discrete Algorithms in 2018. She has served as teaching assistant for numerous courses and as an English to Chinese translator for Scientific American. [CRA bulletin]

CMS postdoctoral scholar Qi (Rose) Yu, working with Professor Anandkumar, and graduate student Stephan Zheng, working with Professor Yue, have won the Best Poster Presentation Award at the 2017 Neural Information Processing Systems (NIPS) Time Series Workshop. Dr. Yu works on the challenge of long-term forecasting in environments with nonlinear dynamics such as those involving climate and traffic data. She is tackling this challenge uses Tensor-Train RNN which are a novel family of neural sequence models that learn nonlinear dynamics directly using higher order moments and high-order state transition functions. [View her poster]

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